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Reseach Article

A Survey on Methods, Attacks and Metric for Privacy Preserving Data Publishing

by Kiran P, Kavya N.p
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 53 - Number 18
Year of Publication: 2012
Authors: Kiran P, Kavya N.p
10.5120/8521-2380

Kiran P, Kavya N.p . A Survey on Methods, Attacks and Metric for Privacy Preserving Data Publishing. International Journal of Computer Applications. 53, 18 ( September 2012), 20-28. DOI=10.5120/8521-2380

@article{ 10.5120/8521-2380,
author = { Kiran P, Kavya N.p },
title = { A Survey on Methods, Attacks and Metric for Privacy Preserving Data Publishing },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 53 },
number = { 18 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 20-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume53/number18/8521-2380/ },
doi = { 10.5120/8521-2380 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:54:24.184962+05:30
%A Kiran P
%A Kavya N.p
%T A Survey on Methods, Attacks and Metric for Privacy Preserving Data Publishing
%J International Journal of Computer Applications
%@ 0975-8887
%V 53
%N 18
%P 20-28
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Privacy Preserving is a prerequisite for most of the existing systems. Data is usually distributed in the system so the main job of Data Publisher is to retrieve information from different location and to transform it in to some standard format suitable for Data Recipient. This information contains sensitive data which must be preserved by Data Publisher before it is published. So the core of this method is to preserve the sensitivity of data pertaining to individual or company related data. The complexity of its representation and the prerequisite of the current industry have driven lot of research in this direction. In this paper, we provide a review of various methods for anonymization and analyze various disclosures that may happen in each of them. We have also discussed various attacks that may take place during anonymization. A comprehensive study of various metric used for measuring anonymity has also been discussed.

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Index Terms

Computer Science
Information Sciences

Keywords

Privacy Preserving Data Mining (PPDM) Privacy Preserving Data Publishing (PPDP) Anonymization Data Mining Metric